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MPI_Reduce_local  Perform a local reduction
#include <mpi.h>
int MPI_Reduce_local(const void *inbuf, void *inoutbuf, int count,
MPI_Datatype datatype, MPI_Op op)
INCLUDE ’mpif.h’
MPI_REDUCE_LOCAL(INBUF, INOUTBUF, COUNT, DATATYPE, OP, IERROR)
<type> INBUF(*), INOUTBUF(*)
INTEGER COUNT, DATATYPE, OP, IERROR
#include <mpi.h>
void MPI::Op::Reduce_local(const void* inbuf, void* inoutbuf,
int count, const MPI::Datatype& datatype, const MPI::Op& op) const
 inbuf
 Address of input buffer (choice).
 count
 Number of
elements in input buffer (integer).
 datatype
 Data type of elements of input
buffer (handle).
 op
 Reduce operation (handle).
 inoutbuf
 Address of in/out buffer (choice).
 IERROR
 Fortran only: Error status (integer).
The global reduce functions (MPI_Reduce_local, MPI_Op_create,
MPI_Op_free, MPI_Allreduce, MPI_Reduce_local_scatter, MPI_Scan) perform
a global reduce operation (such as sum, max, logical AND, etc.) across all
the members of a group. The reduction operation can be either one of a predefined
list of operations, or a userdefined operation. The global reduction functions
come in several flavors: a reduce that returns the result of the reduction
at one node, an allreduce that returns this result at all nodes, and a
scan (parallel prefix) operation. In addition, a reducescatter operation
combines the functionality of a reduce and a scatter operation.
MPI_Reduce_local
combines the elements provided in the input and input/output buffers of
the local process, using the operation op, and returns the combined value
in the inout/output buffer. The input buffer is defined by the arguments
inbuf, count, and datatype; the output buffer is defined by the arguments
inoutbuf, count, and datatype; both have the same number of elements, with
the same type. The routine is a local call. The process can provide one
element, or a sequence of elements, in which case the combine operation
is executed elementwise on each entry of the sequence. For example, if the
operation is MPI_MAX and the input buffer contains two elements that are
floatingpoint numbers (count = 2 and datatype = MPI_FLOAT), then inoutbuf(1)
= global max (inbuf(1)) and inoutbuf(2) = global max(inbuf(2)).
The use of MPI_IN_PLACE is disallowed with MPI_Reduce_local.
The set of predefined operations provided by MPI is listed
below (Predefined Reduce Operations). That section also enumerates the datatypes
each operation can be applied to. In addition, users may define their own
operations that can be overloaded to operate on several datatypes, either
basic or derived. This is further explained in the description of the userdefined
operations (see the man pages for MPI_Op_create and MPI_Op_free).
The operation
op is always assumed to be associative. All predefined operations are also
assumed to be commutative. Users may define operations that are assumed
to be associative, but not commutative. The ‘‘canonical’’ evaluation order of
a reduction is determined by the ranks of the processes in the group. However,
the implementation can take advantage of associativity, or associativity
and commutativity, in order to change the order of evaluation. This may
change the result of the reduction for operations that are not strictly
associative and commutative, such as floating point addition.
Predefined
operators work only with the MPI types listed below (Predefined Reduce
Operations, and the section MINLOC and MAXLOC, below). Userdefined operators
may operate on general, derived datatypes. In this case, each argument that
the reduce operation is applied to is one element described by such a datatype,
which may contain several basic values. This is further explained in Section
4.9.4 of the MPI Standard, "UserDefined Operations."
The following predefined
operations are supplied for MPI_Reduce_local and related functions MPI_Allreduce,
MPI_Reduce_scatter, and MPI_Scan. These operations are invoked by placing
the following in op:
Name Meaning
 
MPI_MAX maximum
MPI_MIN minimum
MPI_SUM sum
MPI_PROD product
MPI_LAND logical and
MPI_BAND bitwise and
MPI_LOR logical or
MPI_BOR bitwise or
MPI_LXOR logical xor
MPI_BXOR bitwise xor
MPI_MAXLOC max value and location
MPI_MINLOC min value and location
The two operations MPI_MINLOC and MPI_MAXLOC are discussed separately below
(MINLOC and MAXLOC). For the other predefined operations, we enumerate below
the allowed combinations of op and datatype arguments. First, define groups
of MPI basic datatypes in the following way:
C integer: MPI_INT, MPI_LONG, MPI_SHORT,
MPI_UNSIGNED_SHORT, MPI_UNSIGNED,
MPI_UNSIGNED_LONG
Fortran integer: MPI_INTEGER
Floatingpoint: MPI_FLOAT, MPI_DOUBLE, MPI_REAL,
MPI_DOUBLE_PRECISION, MPI_LONG_DOUBLE
Logical: MPI_LOGICAL
Complex: MPI_COMPLEX
Byte: MPI_BYTE
Now, the valid datatypes for each option is specified below.
Op Allowed Types
 
MPI_MAX, MPI_MIN C integer, Fortran integer,
floatingpoint
MPI_SUM, MPI_PROD C integer, Fortran integer,
floatingpoint, complex
MPI_LAND, MPI_LOR, C integer, logical
MPI_LXOR
MPI_BAND, MPI_BOR, C integer, Fortran integer, byte
MPI_BXOR
The operator MPI_MINLOC is used to compute a global minimum
and also an index attached to the minimum value. MPI_MAXLOC similarly computes
a global maximum and index. One application of these is to compute a global
minimum (maximum) and the rank of the process containing this value.
The
operation that defines MPI_MAXLOC is
( u ) ( v ) ( w )
( ) o ( ) = ( )
( i ) ( j ) ( k )
where
w = max(u, v)
and
( i if u > v
(
k = ( min(i, j) if u = v
(
( j if u < v)
MPI_MINLOC is defined similarly:
( u ) ( v ) ( w )
( ) o ( ) = ( )
( i ) ( j ) ( k )
where
w = min(u, v)
and
( i if u < v
(
k = ( min(i, j) if u = v
(
( j if u > v)
Both operations are associative and commutative. Note that if MPI_MAXLOC
is applied to reduce a sequence of pairs (u(0), 0), (u(1), 1), ..., (u(n1),
n1), then the value returned is (u , r), where u= max(i) u(i) and r is
the index of the first global maximum in the sequence. Thus, if each process
supplies a value and its rank within the group, then a reduce operation
with op = MPI_MAXLOC will return the maximum value and the rank of the
first process with that value. Similarly, MPI_MINLOC can be used to return
a minimum and its index. More generally, MPI_MINLOC computes a lexicographic
minimum, where elements are ordered according to the first component of
each pair, and ties are resolved according to the second component.
The
reduce operation is defined to operate on arguments that consist of a pair:
value and index. For both Fortran and C, types are provided to describe
the pair. The potentially mixedtype nature of such arguments is a problem
in Fortran. The problem is circumvented, for Fortran, by having the MPIprovided
type consist of a pair of the same type as value, and coercing the index
to this type also. In C, the MPIprovided pair type has distinct types and
the index is an int.
In order to use MPI_MINLOC and MPI_MAXLOC in a reduce
operation, one must provide a datatype argument that represents a pair
(value and index). MPI provides nine such predefined datatypes. The operations
MPI_MAXLOC and MPI_MINLOC can be used with each of the following datatypes:
Fortran:
Name Description
MPI_2REAL pair of REALs
MPI_2DOUBLE_PRECISION pair of DOUBLEPRECISION variables
MPI_2INTEGER pair of INTEGERs
C:
Name Description
MPI_FLOAT_INT float and int
MPI_DOUBLE_INT double and int
MPI_LONG_INT long and int
MPI_2INT pair of ints
MPI_SHORT_INT short and int
MPI_LONG_DOUBLE_INT long double and int
The data type MPI_2REAL is equivalent to:
MPI_TYPE_CONTIGUOUS(2, MPI_REAL, MPI_2REAL)
Similar statements apply for MPI_2INTEGER, MPI_2DOUBLE_PRECISION, and MPI_2INT.
The datatype MPI_FLOAT_INT is as if defined by the following sequence of
instructions.
type[0] = MPI_FLOAT
type[1] = MPI_INT
disp[0] = 0
disp[1] = sizeof(float)
block[0] = 1
block[1] = 1
MPI_TYPE_STRUCT(2, block, disp, type, MPI_FLOAT_INT)
Similar statements apply for MPI_LONG_INT and MPI_DOUBLE_INT.
All MPI objects
(e.g., MPI_Datatype, MPI_Comm) are of type INTEGER in Fortran.
The reduction operators ( MPI_Op ) do not return an error value.
As a result, if the functions detect an error, all they can do is either
call MPI_Abort or silently skip the problem. Thus, if you change the error
handler from MPI_ERRORS_ARE_FATAL to something else, for example, MPI_ERRORS_RETURN
, then no error may be indicated.
The reason for this is the performance
problems in ensuring that all collective routines return the same error
value.
Almost all MPI routines return an error value; C routines
as the value of the function and Fortran routines in the last argument.
C++ functions do not return errors. If the default error handler is set
to MPI::ERRORS_THROW_EXCEPTIONS, then on error the C++ exception mechanism
will be used to throw an MPI::Exception object.
Before the error value is
returned, the current MPI error handler is called. By default, this error
handler aborts the MPI job, except for I/O function errors. The error handler
may be changed with MPI_Comm_set_errhandler; the predefined error handler
MPI_ERRORS_RETURN may be used to cause error values to be returned. Note
that MPI does not guarantee that an MPI program can continue past an error.
MPI_Allreduce
MPI_Reduce
MPI_Reduce_scatter
MPI_Scan
MPI_Op_create
MPI_Op_free
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